



'''simple docstring'''



from typing import List

import numpy as np
def        UpperCAmelCase (    A	:					dict					):
							SCREAMING_SNAKE_CASE       :   int         =      {key: len(A					) for key, value in gen_kwargs.items() if isinstance(A       ,		A					)}
							if len(set(lists_lengths.values()					)					) > 1:
														raise RuntimeError(
														    (
														        '''Sharding is ambiguous for this dataset: '''
														        + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
														        + '''\n'''.join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()					)
														        + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
														        + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
														    )					)
							SCREAMING_SNAKE_CASE       :   Dict         =      max(lists_lengths.values()       ,		default=0					)
							return max(1       ,		A					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      []
							for group_idx in range(A					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
														if num_shards_to_add == 0:
																					break
														SCREAMING_SNAKE_CASE       :   str         =      shards_indices_per_group[-1].stop if shards_indices_per_group else 0
														SCREAMING_SNAKE_CASE       :   int         =      range(A       ,		start + num_shards_to_add					)
														shards_indices_per_group.append(A					)
							return shards_indices_per_group
def        UpperCAmelCase (    A	:					dict       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      _number_of_shards_in_gen_kwargs(A					)
							if num_shards == 1:
														return [dict(A					)]
							else:
														SCREAMING_SNAKE_CASE       :   Dict         =      _distribute_shards(num_shards=A       ,		max_num_jobs=A					)
														return [
														    {
														        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
														        if isinstance(A       ,		A					)
														        else value
														        for key, value in gen_kwargs.items()
														    }
														    for group_idx in range(len(A					)					)
														]
def        UpperCAmelCase (    A	:					List[dict]					):
							return {
							    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
							    if isinstance(gen_kwargs_list[0][key]       ,		A					)
							    else gen_kwargs_list[0][key]
							    for key in gen_kwargs_list[0]
							}






def        UpperCAmelCase (    A	:					np.random.Generator       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {len(A					) for value in gen_kwargs.values() if isinstance(A       ,		A					)}
							SCREAMING_SNAKE_CASE       :   List[Any]         =      {}
							for size in list_sizes:
														SCREAMING_SNAKE_CASE       :   Dict         =      list(range(A					)					)
														rng.shuffle(indices_per_size[size]					)
							# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
							SCREAMING_SNAKE_CASE       :   Tuple         =      dict(A					)
							for key, value in shuffled_kwargs.items():
														if isinstance(A       ,		A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      [value[i] for i in indices_per_size[len(A					)]]
							return shuffled_kwargs









'''simple docstring'''



import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_  :							Tuple             =    'docs/source/en/_toctree.yml'
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      defaultdict(A					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							SCREAMING_SNAKE_CASE       :   Tuple         =      [key for key, value in counts.items() if value > 1]

							SCREAMING_SNAKE_CASE       :   Tuple         =      []
							for duplicate_key in duplicates:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
														if len(A					) > 1:
																					raise ValueError(
																					    F"""{duplicate_key} is present several times in the documentation table of content at """
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''					)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

							# Sort
							return sorted(A       ,		key=lambda A					: s["title"].lower()					)
def        UpperCAmelCase (    A	:					Any=False					):
							with open(A       ,		encoding='''utf-8'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      yaml.safe_load(f.read()					)

							# Get to the API doc
							SCREAMING_SNAKE_CASE       :   Any         =      0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							SCREAMING_SNAKE_CASE       :   Dict         =      content[api_idx]['''sections''']

							# Then to the model doc
							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							SCREAMING_SNAKE_CASE       :   Dict         =      api_doc[model_idx]['''sections''']

							SCREAMING_SNAKE_CASE       :   List[Any]         =      [(idx, section) for idx, section in enumerate(A					) if '''sections''' in section]
							SCREAMING_SNAKE_CASE       :   int         =      False
							for idx, modality_doc in modalities_docs:
														SCREAMING_SNAKE_CASE       :   List[str]         =      modality_doc['''sections''']
														SCREAMING_SNAKE_CASE       :   List[str]         =      clean_model_doc_toc(A					)

														if old_modality_doc != new_modality_doc:
																					SCREAMING_SNAKE_CASE       :   Dict         =      True
																					if overwrite:
																												SCREAMING_SNAKE_CASE       :   int         =      new_modality_doc

							if diff:
														if overwrite:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      model_doc
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      api_doc
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as f:
																												f.write(yaml.dump(A       ,		allow_unicode=A					)					)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							lowerCAmelCase_  :							Any             =    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




